Webinar: Master AI Model Evaluation with Orq.ai Experiments Module

​Evals (short for "evaluations") are automated tests for AI systems that measure whether the system produces correct, high-quality outputs for given inputs. Join us for an in-depth exploration of Orq's newly enhanced Experiments module and learn how to optimize AI model performance.

​What You'll Learn:

​Custom Evaluators: Build your own evaluation logic

• ​Python Evaluators for custom code execution

• ​LLM-as-a-Judge: Leverage language models to evaluate output quality, relevance, and accuracy

• ​JSON Evaluators for structured output validation

• ​Performance Metrics: Monitor cost and latency across single and multi-agent systems

​Advanced Testing Strategies:

• ​A/B Testing: Compare different prompts or model configurations side-by-side to determine what works best for your use case

• ​Regression Testing: Detect unintended changes in outputs after modifying prompts or configurations, ensuring stability

• ​Backtesting: Assess how new setups would have performed on historical data before deploying to production

​Who Should Attend

• ​ML Engineers and Data Scientists optimizing LLM applications

• ​Product teams implementing AI features

• ​Engineering leaders establishing AI quality standards

• ​Anyone looking to move from manual testing to systematic evaluation